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Cognitive Offloading to AI — Is Outsourcing Your Thinking Eroding Critical Thinking?

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Cognitive Offloading to AI — Is Outsourcing Your Thinking Eroding Critical Thinking?
  • Target audience: Software engineers who use AI tools on a daily basis
  • Prerequisites: Basic experience using AI tools such as GitHub Copilot and ChatGPT
  • Reading time: 12 minutes

Overview

Cognitive offloading is the act of delegating cognitive processing to external tools. Just as smartphones have made us stop memorizing phone numbers, the practice of outsourcing “thinking” to AI is rapidly spreading. A study by Gerlich (2025) involving 666 participants found a strong negative correlation of r = -0.68 between AI usage frequency and critical thinking scores. However, this phenomenon involves age differences and buffering effects of education, making it far from a simple causal relationship. This article focuses on the mechanisms and psychological effects of cognitive offloading, examining the evidence and exploring how engineers can protect their capacity to think. For empirical data on skill degradation resulting from offloading, see the AI Deskilling Paradox article.

Is “Not Having to Think” Really a Good Thing?

Cognitive offloading refers to the practice of delegating cognitive tasks — tasks that would normally be processed internally — to external tools or devices. Taking notes, setting calendar reminders, using a calculator — these are all forms of cognitive offloading.

Humans have always engaged in cognitive offloading. The invention of writing itself was an offloading of memory. In that sense, cognitive offloading to AI is not historically unprecedented.

However, cognitive offloading to AI involves a qualitative difference. Writing and calculators replaced specific cognitive functions (memory, calculation), but LLMs can replace cognitive processes in general — analysis, reasoning, judgment, and problem structuring1. The scope of offloading has expanded from “outcomes” to “processes.”

To put this in an engineer’s everyday context:

  • Instead of analyzing the root cause of a bug yourself, you paste the error log into an AI
  • Instead of thinking through design alternatives yourself, you ask the AI “What’s the best approach?”
  • Instead of searching for problems with your own eyes during code review, you check the AI review results

These practices improve productivity. But what happens when we continue to outsource the act of “thinking” itself?

Gerlich (2025): What a Large-Scale Study of 666 Participants Reveals

Study Design

Gerlich (2025) published a study in the journal Societies that conducted a large-scale investigation into the relationship between AI usage and critical thinking1.

ItemDetails
Participants666 (diverse ages and educational backgrounds)
MethodMixed methods (quantitative survey + qualitative interviews)
MeasuresAI usage frequency, critical thinking scores
PublicationSocieties, MDPI

Key Findings

Correlation between AI usage frequency and critical thinking scores: r = -0.68 (p < 0.001)

This correlation coefficient is exceptionally strong for social science research. Participants with higher AI usage frequency showed a clear tendency toward lower critical thinking test scores1.

flowchart TB
    subgraph RESULT["Key Findings"]
        R1["AI usage frequency vs.<br>critical thinking score<br>r = −0.68<br>(p < 0.001)"]
        R2["Ages 17–25 showed<br>highest AI dependence<br>and lowest critical<br>thinking scores"]
        R3["Higher education<br>functioned as a<br>protective factor"]
    end

Age Differences

The age differences are particularly noteworthy:

Age GroupAI DependenceCritical Thinking Score
17–25HighestLowest
26–45ModerateModerate
46+LowestHighest

The 17–25 age group showed the highest AI dependence and the lowest critical thinking scores1. However, interpreting these results requires caution.

Correlation or Causation?

The biggest limitation of this study is that it is a cross-sectional study. Whether AI usage reduced critical thinking, or whether people who already had lower critical thinking were more likely to rely on AI, cannot be determined from this study design alone.

Three hypotheses about the direction of causality are possible:

  1. AI usage → decline in critical thinking (cognitive offloading hypothesis)
  2. Low critical thinking → increased AI usage (self-selection hypothesis)
  3. Third variable (age, education, attitudes toward technology, etc.) → influences both

Gerlich himself acknowledges this limitation. Note that a Correction has been published regarding an error in Table 4 (duplication with Table 3), though the author states this does not affect the scientific conclusions1.

An r = -0.68 is an impressive figure, but it would be premature to conclude from a cross-sectional correlation that “AI destroys critical thinking.” This study provides “grounds for concern” but is not “proof of causation.”

Supplementary Qualitative Data

In addition to quantitative data, the qualitative interviews yielded suggestive findings1:

  • Many participants reported that “since I started using AI, I ask the AI before researching things myself”
  • A self-awareness that “I increasingly use AI responses as-is, and spend less time thinking on my own”
  • On the positive side, reports that “AI has enabled me to learn new topics in greater depth”

The Two Faces of Cognitive Offloading

A 2025 paper published in Frontiers in Psychology analyzes how AI-driven cognitive offloading has both “cognitive offloading” and “cognitive overload” dimensions2.

The Positive Side

Cognitive offloading has inherently beneficial aspects:

  • Freeing cognitive resources: Delegating routine tasks to AI allows you to concentrate cognitive resources on more important tasks
  • Adaptive coping with stress: Reducing excessive cognitive load can help prevent burnout
  • Reducing working memory load: Decreasing the burden of processing multiple pieces of information simultaneously can improve decision-making quality

In engineering, delegating boilerplate code generation and format conversion to AI frees you to focus on architecture design and problem analysis. This is rational offloading.

The Negative Side

On the other hand, the same paper identifies the following risks2:

  • Reduced opportunities for introspection: Outsourcing cognitive processing eliminates opportunities to reflect on your own thinking processes
  • Excessive dependence on algorithmic feedback: AI’s criteria begin to replace your own judgment criteria
  • Anxiety from hypermonitoring and optimization: Constantly chasing AI suggestions amplifies “anxiety about not being optimal”
flowchart TB
    CO["Cognitive Offloading"]
    CO --> POS["Positive"]
    CO --> NEG["Negative"]

    POS --> P1["Freeing cognitive<br>resources"]
    POS --> P2["Stress reduction"]
    POS --> P3["Focus on<br>critical tasks"]

    NEG --> N1["Reduced<br>introspection"]
    NEG --> N2["Algorithm<br>dependence"]
    NEG --> N3["Optimization<br>anxiety"]

    classDef positive stroke:#2ea44f,stroke-width:2px
    classDef negative stroke:#d29922,stroke-width:2px
    class POS,P1,P2,P3 positive
    class NEG,N1,N2,N3 negative

Transformation of the Mental Architecture of Coping

The most provocative insight of this paper is its argument that AI transforms the “mental architecture of coping”2.

“Coping” refers to cognitive strategies for dealing with stress and difficulty. When facing a problem, humans mobilize internal cognitive resources to cope. But in an environment where AI is always available, “asking the AI first” becomes the lowest-cost coping strategy.

As a result, the threshold for activating internal coping strategies — trial and error, hypothesis testing, conceptual restructuring — rises. There is a risk that the pattern of reaching for AI as a crutch before exercising one’s “thinking muscles” becomes entrenched.

The Vulnerability of Digital Natives

Why Younger People Are Particularly Affected

Structural factors underlie the finding that 17–25-year-olds showed the highest AI dependence in Gerlich’s study1:

  1. A generation that grew up in a world where AI is taken for granted: They may have started using AI before developing critical thinking skills
  2. A tendency to avoid cognitive load: Generations accustomed to digital tools are more inclined to choose the cognitively easier path
  3. Changes in learning style: The process of “research → understand → remember” is shortened to “ask AI → get answer”

Why Older Adults Show Relative Resilience

Meanwhile, factors behind the 46+ age group’s relative maintenance of critical thinking:

  1. Accumulated experience thinking without AI: Years of independent problem-solving have built a cognitive foundation
  2. Critical thinking is already established: Critical thinking has become habitual through education and professional experience
  3. A tendency to position AI as an “auxiliary tool”: Generational psychological distance from technology

However, this is not a story about “older people being superior.” Younger people may have advantages in new skills for leveraging AI (prompt design, integrating AI output, workflow construction). The concern is the risk of habituating cognitive offloading to AI before the foundation of critical thinking has been established.

The Buffering Role of Education

The Protective Effect of Higher Education

The most constructive finding in Gerlich’s research is that higher education functions as a buffer against AI dependence1.

Participants with higher education relatively maintained their critical thinking scores even when their AI usage frequency was high. The protective elements that education provides may include:

  • Metacognitive skills: The ability to monitor your own thinking processes. Being able to recognize “Am I letting AI think for me right now, or am I thinking for myself?”
  • Habits of critically evaluating information: Training in assessing source reliability and comparing multiple perspectives
  • Training in the “power to question”: An attitude of not uncritically accepting even authoritative sources

Implications for Engineering Education

This finding also has implications for the education and development of engineers.

When junior engineers begin using AI tools, what matters is not only “how to use AI” but also ensuring they have sufficient experience with “problem-solving without AI.” The fundamentals of debugging processes, code reading methods, and evaluating design trade-offs — if cognitive offloading to AI becomes habitual before these foundations are established, the risk of entering a positive feedback loop of deskilling increases.

Cognitive Offloading for Engineers: What to Delegate and What to Retain

“Outcome” Offloading vs. “Process” Offloading

Drawing on the research findings, I want to propose a useful distinction for thinking about the impact of cognitive offloading. It comes down to what you are offloading:

Outcome offloading (low risk):

  • Generating boilerplate code (boilerplate, CRUD operations)
  • Regular expression and SQL query syntax
  • Document format conversion
  • These outsource “answers” while retaining “ways of thinking”

Process offloading (high risk):

  • Root cause analysis of bugs
  • Architecture decision-making
  • Security risk assessment
  • Problem detection during code review
  • These outsource “ways of thinking” themselves, carrying a high risk of cognitive skill degradation

This distinction is not a clean binary — it exists on a gradient. What matters is being aware of whether what you are currently offloading is an “outcome” or a “process”.

Practical Guidelines

SituationRecommended ApproachRationale
Boilerplate generationFine to delegate to AIOutcome offloading with low cognitive value
DebuggingForm your own hypotheses first, then verify with AIExperiencing the process is crucial for skill maintenance
Design decisionsTreat AI’s proposals as “one of several options”Preserves the judgment process
Code reviewReview with your own eyes before using AI reviewPrevents atrophy of problem-detection skills
Learning new technologyImplement it yourself after reading AI’s explanationDon’t skip the process of understanding

Discussion: How to Protect Your “Capacity to Think” in an Age of Outsourced Thinking

Drawing Conscious Lines Between “What to Offload and What to Think Through Yourself”

Synthesizing the research findings, the issue with cognitive offloading is not “whether to use AI” but whether you are consciously drawing the line between “what to delegate and what to retain.”

Unconscious offloading — reflexively tossing every problem to AI the moment you encounter it — is likely one factor behind the negative correlation shown in Gerlich’s study. In contrast, conscious offloading — using AI after deliberately deciding “this part I can leave to AI, this part I should think through myself” — is a strategic allocation of cognitive resources and, in fact, an exercise of critical thinking itself.

Using AI as a Tool for “Questions” Rather Than “Answers”

One approach to minimizing the impact of cognitive offloading while still reaping AI’s benefits is to use AI not as a “tool for getting answers” but as a “tool for deepening questions.”

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[Pattern where offloading progresses]
Problem occurs → Ask AI → Adopt answer → Next problem

[Pattern that preserves thinking]
Problem occurs → Form your own hypothesis → Ask AI "What are the flaws in this hypothesis?"
→ Evaluate AI's critique yourself → Refine hypothesis → Next problem

In the latter pattern, AI functions as a “critic,” and the thinking process remains on the human side. This is a practice of metacognitive monitoring and refinement.

Periodically Reclaiming “Process” Offloading

The framework distinguishing “outcome offloading” and “process offloading” from the previous section is useful here as well. The key is to periodically perform the processes you normally delegate to AI by yourself.

  • Debugging: Before handing the error log to AI, try to formulate three hypotheses on your own
  • Design: Before looking at AI’s proposals, write out the trade-off axes yourself
  • Learning: After reading AI’s explanation, try summarizing it in your own words

This connects to a concept in cognitive science known as “desirable difficulty.” Moderate cognitive load reduces efficiency in the short term but promotes long-term learning and memory consolidation. That said, quantitative guidelines for exactly how often this should be done do not yet exist.

Summary

Here is a summary of the research findings on cognitive offloading.

Notable findings (though additional research is needed to establish causality):

  • A strong negative correlation of r = -0.68 was found between AI usage frequency and critical thinking scores (n = 666)1
  • The 17–25 age group showed the highest AI dependence and the lowest critical thinking scores1
  • Higher education functions as a buffer against cognitive offloading1

Theoretical findings:

  • Cognitive offloading has both a positive dimension — freeing cognitive resources — and negative dimensions — reduced introspection and algorithm dependence2
  • AI can transform the “mental architecture of coping”2

Practical implications:

  • The issue is not “whether to use AI” but drawing a conscious line between “what to delegate and what to retain”
  • “Outcome” offloading and “process” offloading differ qualitatively in their impact
  • Intentionally imposing cognitive load through “intellectual strength training” is likely effective for skill maintenance, but validating specific methods remains a challenge for future research

Cognitive offloading itself is a historical human strategy, and there is no need to reject it outright. However, the scale and speed of AI create qualitatively different risks from previous tools (writing, calculators, GPS). Education and metacognition — the ability to recognize “what am I currently thinking about, and what am I not” — are the most fundamental defenses against this risk.

For more on this topic, see the following related articles:

References

References are listed in the order of their citation numbers in the text.

Additional References (not cited by number in the text)

  1. AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking - Gerlich, M. Societies, 15(1), 6 (2025). Mixed-methods study of 666 participants. Correlation between AI usage frequency and critical thinking scores: r = -0.68 (p < 0.001). Peer-reviewed. A Correction regarding a Table 4 error has been published, but the author states it does not affect the scientific conclusions. [Reliability: Medium–High] ↩︎ ↩︎2 ↩︎3 ↩︎4 ↩︎5 ↩︎6 ↩︎7 ↩︎8 ↩︎9 ↩︎10 ↩︎11

  2. Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping - Chirayath, G., Premamalini, K., & Joseph, J. Frontiers in Psychology, 16 (2025). Analyzes how AI has both cognitive offloading and cognitive overload dimensions. Peer-reviewed. [Reliability: High] ↩︎ ↩︎2 ↩︎3 ↩︎4 ↩︎5

AI・Technology
AI Cognitive-Offloading Critical-Thinking Cognitive-Science Education
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